Lobbying, Laws, and Logistic Regression

Nidhi a -

This week, my project took a major step forward! In data collection AND my entire approach to my methodology. One of my biggest highlights was getting the opportunity to visit the Arizona State Capitol for Charter Day. I had the chance to speak with legislators about my research and learn from those who definitely know more about campaigning and laws than I do. Seeing policymaking up close and conversing with our representatives helped me refine how I measure the influence of campaign finance on legislative decisions. It also gave me a clearer picture of how lawmakers themselves view donations — something crucial as I analyze my data. 

A major realization this week was that I needed to define “Latino” in my research. Am I measuring this group ethnically, culturally, or both? After reflecting on this for the better part of the week, I decided to use a broad definition that accounts for both ethnic identity and cultural affiliation. I also refined my law selection process by working backward. Starting with the bills and then tracking the financial influence behind them seems more appropriate. For Arizona, I chose SB 1070. This law, passed in 2010 gave law enforcement broad jurisdiction to check immigration status of anyone they suspected of being undocumented. It disproportionately targeted Latino communities and led to nationwide racial profiling. Since corporate PACs invested in private prisons and law enforcement could stand to benefit from increased detentions, hopefully this law will be an example of financial influence on policy making. 

Additionally, I realized that organizing the data was going to take longer than expected. To ensure I get through the most important cases I’m prioritizing them in the following order:

  1. Arizona
  2. Texas
  3. California
  4. Florida

Finally, after realizing my methodology will have to change again, I needed a method that can quantify the relationship between donations and legislative votes. That’s something I’m still looking into, but for now, I’m debating between logistic regression and a chi squared test!

I’ve built a spreadsheet that splits my data into such categories that I’ll be using in the tests I choose:

  • Legislator Name
  • State & Party Affiliation
  • Vote (Yes/No) on the Bill
  •  Total Donations Received ($)
  •  Industry Source of Donations (Private Prisons, Agriculture, etc.)
  • % Latino Population in District
  • Past Voting Record on Similar Issues

I plan on deciding my method and running my first tests next week and I’m super excited for what’s to come!

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Comments:

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    sanjana_b
    It's so cool that you got to attend an event at the capitol! Did you learn any valuable trends or information from the legislators there?
    krithika_j
    Hey Nidhi! Being able to talk to our lawmakers one on one must have been super eye-opening! Were there any surprising differences in how legislators view donations? This is super insightful research and I'm excited to see what results you find!
    smilangi_s
    What a wonderful experience, Nidhi! I hope we’ll get to see some pictures soon! Are there often legislators who receive funding from a certain industry but still vote against the expected industry interests? How do you plan to account for such scenarios?

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